1. Field of the Invention
This invention relates to the field of video surveillance, and in particular to a system and method for tracking objects that are occluded while traversing a surveillance scene, or occluded as the object traverses from one scene to another.
2. Description of Related Art
Video surveillance systems often include object-tracking capabilities, wherein an identified object, or target, is continually identified as the object travels within a scene, or set of scenes. Generally, multiple objects are simultaneously tracked. The objects may include people, vehicles, parcels, and so on.
In a typical surveillance system, one or more cameras are strategically placed to optimize the video coverage provided. As an object traverses a camera's field of view, an object-tracking module searches each sequential image frame for the target, based on the appearance of the target within a prior image frame. The appearance of the target in each sequential frame will not generally be identical to its appearance in the prior frame, due to changes in the object's posture and orientation as it moves, or due to changes in lighting conditions, camera angle, as the object enters different areas, and so on. Generally, the amount of change from frame to frame is limited, and the identification of a similar, albeit not identical, object in each sequential frame is fairly straightforward.
Object-tracking becomes more difficult if an object disappears from a scene, then reappears at a different location in the scene, or in a different scene. Such a disappearance-then-reappearance, herein termed a temporary occlusion, can occur, for example, as the target passes behind an obstacle, such as a pillar or tree, as the target exits through a door from one surveillance scene to another, as the target enters then exits a non-surveyed area, such as a rest-room or closet, and so on. The difficulty arises from the fact that the target's reappearance differs in time and/or space from when and/or where it disappeared. The lighting conditions may differ on each side of an obstacle, the object may change appearance while behind the obstacle, the camera angle may differ on each side of a door, a different object may appear at the other side of the obstacle, or in the other scene, while the target is still occluded, and so on. The difficulty is further compounded because some objects may disappear and not re-appear in a scene, for example when a target leaves the surveyed area, or areas, entirely.
Conventional systems generally predict the appearance and location of a target in each successive image frame, and typically extend the concept to predict the appearance and location of an occluded target in future frames, based on the prior trajectory of the target before its disappearance. U.S. Pat. No. 5,280,530, “METHOD AND APPARATUS FOR TRACKING A MOVING OBJECT”, issued 18 Jan. 1994 to Trew et al., and U.S. Pat. No. 6,263,088, “SYSTEM AND METHOD FOR TRACKING MOVEMENT OF OBJECTS IN A SCENE”, issued 17 Jul. 2001 to Crabtree et al., are examples of such techniques, and are incorporated by reference herein.
In “BAYESIAN MULTI-CAMERA SURVEILLANCE”, Proceedings of Computer Vision and Pattern Recognition, 1999 II:253-259, V. Kettnaker and R. Zabih describe an object-tracking system wherein a “chain of appearances” is created for a tracked object as the object traverses the fields of view of multiple cameras. In addition to using the visual data, the system uses a stochastic model that mathematically expressed how likely it was that an object last seen in one camera would be observed next in a different camera at a particular time. To facilitate the tracking of multiple objects, the image information from all of the cameras is stored in a common database, and various possible object paths are postulated. Using a Bayesian model, the optimal solution is the set of object paths with the highest posterior probability, given the observed data. The maximum a posteriori solution is approximated using linear programming techniques.